Evaluation of QRS morphological classifiers in the presence of noise
Computers and Biomedical Research
Time Series Segmentation Using a Novel Adaptive Eigendecomposition Algorithm
Journal of VLSI Signal Processing Systems
Time Series Segmentation for Context Recognition in Mobile Devices
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Automatic segmentation of piecewise constant signal by hidden Markov models
SSAP '96 Proceedings of the 8th IEEE Signal Processing Workshop on Statistical Signal and Array Processing (SSAP '96)
Optimal Time Segmentation for Signal Modeling and Compression
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 3 - Volume 3
Signal segmentation and denoising algorithm based on energy optimisation
Signal Processing
Automatic decomposition of time series into step, ramp, and impulse primitives
Pattern Recognition
Simultaneous speech segmentation and phoneme recognition using dynamic programming
ICASSP '96 Proceedings of the Acoustics, Speech, and Signal Processing, 1996. on Conference Proceedings., 1996 IEEE International Conference - Volume 06
Segmentation of specific speech signals from multi-dialog environment using SVM and wavelet
Pattern Recognition Letters
Effectiveness of signal segmentation for music content representation
MMM'08 Proceedings of the 14th international conference on Advances in multimedia modeling
Optimal segmentation of random processes
IEEE Transactions on Signal Processing
Bayesian curve fitting using MCMC with applications to signalsegmentation
IEEE Transactions on Signal Processing
Change detection in teletraffic models
IEEE Transactions on Signal Processing
Exact Bayesian curve fitting and signal segmentation
IEEE Transactions on Signal Processing
Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering
Computer Methods and Programs in Biomedicine
Hi-index | 0.00 |
Biomedical signals are nonstationary in nature, namely, their statistical properties are time-dependent. Such changes in the underlying statistical properties of the signal and the effects of external noise often affect the performance and applicability of automatic signal processing methods that require stationarity. A number of methods have been proposed to address the problem of finding stationary signal segments within larger nonstationary signals. In this framework, processing and analysis are applied to each resulting locally stationary segment separately. The method proposed in this paper addresses the problem of finding locally quasi-stationary signal segments. Particularly, our proposed algorithm is designed to solve the specific problem of segmenting semiperiodic biomedical signals corrupted with broadband noise according to the various degrees of external noise power. It is based on the sample entropy and the relative sensitivity of this signal regularity metric to changes in the underlying signal properties and broadband noise levels. The assessment of the method was carried out by means of experiments on ECG signals drawn from the MIT-BIH arrhythmia database. The results were measured in terms of false alarms based on the changepoint detection bias. In summary, the results achieved were a sensitivity of 97%, and an error of 16% for records corrupted with muscle artifacts.